To analyze your own data —
why does it have to start with a tool?
Bring the data — Skari handles method, validation, and interpretation.
01 · DOMAINS
Six analysis areas, one workbench.
Pick the area that fits your question — each opens a purpose-built set of methods.
Statistics, finance, marketing, quality, decision, optimization. The question decides the domain; the domain decides the method menu — you never have to translate a business question into a test name on your own.
17 results
Simple Linear Regression
Statistics › Relationship › Regression
Multiple Linear Regression
Statistics › Relationship › Regression
Ridge / Lasso Regression
Statistics › Relationship › Regression
Logistic Regression
Statistics › Relationship › Regression
One search, across all 6 domains — 200+ analyses.
02 · START
Three ways to put data in.
Sample data, file upload, or drag and drop.
Not ready with your own file? Load a sample dataset and see a full result in one click. When you are ready, upload a CSV or Excel file — or just drop it on the screen. Column types are detected automatically and each variable is tagged numeric or categorical, so variable selection starts from a sensible default.
One click loads a dataset with the analysis already set up.
CSV and Excel (.csv, .xlsx, .xls) from your own work.
Drop the file on the screen and the analysis starts.
Drop your file to upload
or click to browse · or start from a sample dataset
Column types detected automatically — n = 428 rows, 4 variables.
03 · RECOMMEND
Not sure which analysis fits?
Describe the question — the AI picks the method.
The hardest part is rarely the computation — it is knowing which method your data allows. Skari scans every column, weighs variable types and sample size, and lists the analyses that actually apply. Prefer to browse? Split the catalog into Explore and Model modes, or search it by name.
Every column scanned — only the methods your data allows.
04 · FLOW
One structure — the same everywhere.
Every method in every domain runs through the same six steps.
Variables → Settings → Validation → Summary → Reasoning → Report. Each step explains itself as you go, so the second analysis feels familiar even when the method is new — and you can always step back without losing the run.
Choose the columns the analysis runs on
Pick the roles the method needs — group and outcome for a t-test, target and features for a regression.
Outcome · required
Predictors · required
Covariates · optional
Drag a column here
Roles auto-guessed from column names.
Next05 · VALIDATE
Problems caught before you run, not after.
Assumptions and data quality are checked up front.
Before a single request goes out, Skari checks that your variables suit the method, that the sample is large enough, and that assumptions such as normality and equal variance are not violated. Each check reads pass, warning, or fail — and a hard fail blocks the run instead of returning a number you should not trust.
A hard fail blocks “Run Analysis” — no untrustworthy result.
06 · INTERPRET
Numbers are not the answer.
The verdict, the reasoning, and what to do next.
The summary gives the headline result with a quality grade across dimensions such as fit, reliability, precision, sample adequacy, and assumptions. “Why this result?” then explains what drove it, and the AI read-out turns the tables into sentences you can put in front of someone else — with alternative methods suggested when your data would fit a different one better.
Result strength
Strong
Reliability
Good
Precision
Moderate
Assumptions
Review
AI interpretation
The model is highly significant and explains a large share of the variation. Check multicollinearity before reading individual coefficients.
07 · REPORT
End with something you can hand in.
Word report, tables, charts — and the code that reproduces it.
Results arrive as visualizations and tables, with significance marked against p-values and an insight note under each table. Export an APA-formatted Word report, a CSV of the tables, a PNG of the screen, or the R / Python script that reproduces the same analysis in your own environment — and save the whole run to a project so you can come back to it.
The exported script is a starting point, not a black box — adjust the column names and options to match your own environment, then attach it to a paper or assignment as reproducible evidence.
08 · CATALOG
Inside Statistics: nine families, 120+ analyses.
From descriptives to structural equation models — the whole range runs through the same six steps.
Descriptives · frequency · correlation · power
t-tests · ANOVA family · non-parametric · Bayesian
Linear · logistic · GLM · Ridge & Lasso
Decision tree · random forest · XGBoost · ANN
Kaplan-Meier · Cox · competing risks · RMST
DID · PSM · RDD · IV · panel models
EFA · CFA · PCA · mediation · SEM
K-means · hierarchical · DBSCAN · GMM
ARIMA · Prophet · VAR/VECM · GARCH
ALSO IN YOUR DATA LAB
Beyond your own data.
Build models, run surveys, or explore public statistics — same platform, different lens.
Bring your data. We’ll handle the method.
AI picks the right analysis, validates it, and walks you to a report — across six domains.